TopoGroups: Context-Preserving Visual Illustration of Multi-Scale Spatial Aggregates

論文URL:http://dl.acm.org/citation.cfm?doid=3025453.3025801

論文アブストラクト:Spatial datasets, such as tweets in a geographic area, often exhibit different distribution patterns at multiple levels of scale, such as live updates about events occurring in very specific locations on the map. Navigating in such multi-scale data-rich spaces is often inefficient, requires users to choose between overview or detail information, and does not support identifying spatial patterns at varying scales. In this paper, we propose TopoGroups, a novel context-preserving technique that aggregates spatial data into hierarchical clusters to improve exploration and navigation at multiple spatial scales. The technique uses a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates. Our user study explores multiple visual encoding strategies for TopoGroups including color, transparency, shading, and shapes in order to convey the hierarchical and statistical information of the geographical aggregates at different scales.

日本語のまとめ:

スケールの違いと地図情報を別々に可視化し、同一ディスプレイに複数のスケールを表示することによって、ユーザの理解をより向上させるシステムを作成した。被験者実験を通して、境界線や種別ごとの適切な配色を議論した。

(103文字)

発表スライド: